The Regional Analysis and Forecast System (RAFS, Hoke et al., 1989) was implemented
by the National Meteorological Center (now the National Centers for Environmental Prediction
(NCEP)) on 27 March 1985. These are the major milestones in the development of RAFS:

The August 1991 implementation constituted the "final" set of changes to the RAFS. The
system was frozen after August 1991 to avoid compromising the efforts of the Techniques
Development Laboratory (TDL) to develop Model Output Statistics (MOS) guidance from a
fixed configuration of the NGM.

II. Rationale for changes to the RAFS

The 27 September 1999 fire at the NOAA Central Computing Facility in Suitland,
Maryland, which destroyed NCEP's Cray C-90 supercomputer, necessitated moving the RAFS to
a backup system on a slower Cray J-90 machine. Although the new IBM-SP Class VIII computer
was installed and undergoing testing by this time, the NGM had not yet been converted to run on
this computer. Since the backup Cray is 3-4 times slower than the C-90, there is no time to run
the RDAS. Therefore, the RAFS uses a GDAS forecast as first guess to the ROI analysis. With
the RAFS running on a slower computer, NGM products are now available for users about one
hour later then they were before the fire.

Anticipating the possible discontinuance of the NGM MOS products in the future, TDL
has been developing MOS guidance from the Aviation (AVN) run of the NCEP Global Spectral
Model. A one year test period is planned during which both the AVN and NGM MOS will be
available to NWS and private sector forecasters. TDL anticipates that the full AVN MOS package
will be ready for distribution by April 2000. Since there are no funds available to keep the Cray J-90 computer in operations beyond March 2000, a capability to run the NGM on the IBM-SP
computer is required. Conversion of all codes to run both the NGM and the RDAS, with the
redesign of the NGM to use the new Message Passing Interface (MPI) on the IBM, would take
approximately 2 person years. Such a concentrated effort of this magnitude, which would severely
impact development of the NCEP Eta mesoscale model, was deemed not to be cost effective.

Instead, a strategy has been developed to allow for conversion of fewer RAFS codes to
the IBM SP. This calls for the NGM to be initialized from the "Early" Eta 3-d variational analysis
(3DVAR; Rogers et al., 1998, 1999) over North America and from a GDAS forecast over the rest
of the hemisphere. This strategy also calls for running all codes on a single node of the IBM-SP,
thus eliminating the need for MPI. While the NGM code runs slower on the IBM than it did on
the Cray C-90, use of the Early Eta analysis instead of the ROI analysis to initialize the model
allows for the NGM job to be started approximately 45-60 minutes sooner. With this earlier start
the NGM will finish at roughly the same time as it did on the Cray C-90 computer, or roughly 60-70 minutes sooner than it is currently finishing on the slower Cray J-90.

III. The NGM on the IBM-SP : Configuration and differences from the Cray
version

Fig. 1 shows the two-grid system which has been used in the NGM since 1991. Since the
demise of the RDAS, the RAFS suite starts by getting the best available NCEP Global Spectral
Model forecast and interpolating it vertically to the 16 NGM vertical levels and horizontally to all
NGM grid points. At T+2:10 h (T=00Z or 12Z) all available observations within both grids are
ingested and the difference between the GDAS forecast values and observations (called the
observation increment) is obtained. After quality control is performed to remove unrealistic
observations, a ROI analysis of the observed increments is performed to get the analyzed
increment. The analysis increment is then added to the GDAS forecast to get the full-field
analysis. After the implicit normal mode initialization is performed, a 48-h NGM forecast is done.

Fig. 2 shows the new two-grid configuration of the NGM. Since the Early Eta domain
(dotted outline in Fig 2) does not extend beyond the North Pole into Europe and Asia, the higher
resolution B-grid was modified to be similar in size to the Eta computational grid. Thus, to
perform an NGM forecast on the IBM-SP, the following steps are taken:

• The best available NCEP global spectral model forecast (usually the 6-h forecast from the
NCEP Final 06Z or 18Z analysis) is interpolated to both NGM grids.

• The Eta analysis is interpolated to NGM vertical levels and to all NGM B-grid points
within the computational domain of the Eta model, replacing the values from the GDAS forecast in
the NGM initial condition file. For B-grid points outside of the Eta domain, the GDAS forecast is
used. Since the top of the Eta model is at 25 mb, one would have to extrapolate the Eta analysis to
get values in the top layer of the NGM. To avoid potential problems caused by vertical
extrapolation of the Eta analysis, the values from the GDAS forecast are used in the top NGM
layer.

• The implicit normal mode initialization is performed.

• A 48-h NGM forecast is done, with boundary values for the fine resolution B-grid
obtained from the forecast on the coarse resolution A-grid.

There are potential advantages and disadvantages by using the Eta analysis to initialize the
NGM:

ADVANTAGES

• Possible positive impact of observation types used by the Eta 3-d variational analysis
which are not used by the Regional OI analysis, such as aircraft temperatures, surface winds over
land, VAD winds from NEXRAD radars, GOES (land and ocean) and SSM/I (ocean only)
retrieved precipitable water data, and SSM/I oceanic surface winds.

• Possible positive benefit to the NGM forecast through improvements to the Eta analysis,
such as direct use of satellite radiances, NEXRAD radial velocities, and assimilation of hourly
precipitation data.

DISADVANTAGES

• Possible degradation due to the lack of an analysis update on all of the A-grid and any
part of the B-grid outside of the Eta model domain.

• Since the Eta runs in the NCEP early slot, the data cutoff time is 71 minutes after 00Z or
12Z, as opposed to 127 minutes after 00Z or 12Z for the RAFS. Therefore, any data which arrives
after 0111Z or 1311Z would not be available for use in the Eta analysis and by default, in the
NGM initialized from that analysis. An example of the difference in data coverage is shown by the
plot of satellite temperature retrievals available to the Eta analysis (Fig. 3) and the RAFS analysis
(Fig. 4) at 00Z 14 February 2000. For the Eta analysis, satellite temperature data coverage was
limited to the area south of 35N and west of 130W. Since the RAFS has a later data cutoff time, it
obtained additional TOVS data located further north and west. The Eta uses TOVS data with
higher resolution (80 km vs. 220 km in the RAFS), which explains the higher data density. The
rawinsonde coverage for the Eta and RAFS analyses at the same time is shown in Figs. 5 and 6,
respectively. For this time all North American stations were used in both analyses, including those
in Mexico (which occasionally arrive too late to be included in the Eta analysis). Also at this time
there are three dropwindsonde observations east of Hawaii and two in the Atlantic which were
transmitted too late to be included in the Eta analysis.

• Any systematic bias in the Eta model could be introduced to the NGM since the Eta
model is initialized with the Eta Data Assimilation System (EDAS, Rogers at al 1996), which
consists of 3-h Eta model forecasts and analysis updates. Since June 1998 the EDAS has been run
with full cycling of atmospheric variables and soil parameters.

IV. Verification

Two parallel tests of the new version of the NGM have been made: the current parallel test
which started at 12Z 16 December 1999 and a warm season retrospective test from 2 July - 2
August 1999. The complete set of verification charts for both tests can be found at
http://sgi62.wwb.noaa.gov:8080/NGMSTAT/ . Computed were 1) the forecast bias and root-mean-square (RMS) error versus rawinsonde height, wind, temperature, and relative humidity data
over the contiguous US (CONUS) and Alaska, 2) both time series and cumulative bias / RMS
errors of forecast surface temperatures over the CONUS, and 3) 24-h forecast precipitation skill
scores over the CONUS. Highlights from these results are presented below:

a. Fits to Rawinsondes - Cold Season

The vertical distribution of the RMS error versus rawinsondes over the CONUS for the 12-h, 24-h, and 48-h operational and parallel NGM forecasts from 16 December 1999 - 14 February
2000 are shown in Fig. 7 (height), Fig. 8 (temperature), Fig. 9 (vector wind) and Fig. 10 (relative
humidity. For height, temperature and wind there are small differences in RMS error at all 3
forecast times between the surface and 500 mb. Above 500 mb there is a tendency for the parallel
NGM to have higher RMS errors, especially for geopotential heights. For winds, the greatest
difference between the operational and parallel NGM is at the jet stream level between 300 and
200 mb. In examination of the 00-h Eta and parallel NGM 250 mb wind charts, the author
occasionally observed that wind maxima in the Eta analysis (such as the subtropical jet in the
eastern Pacific) would be lower by 10-15% after interpolation to the NGM vertical levels. Vertical
resolution at jet level of the current 32-km, 45 level Eta model is 25-30 mb, while in the NGM it is
70-75 mb. It is possible that details are lost during the vertical interpolation (especially for intense,
narrow jet streaks) of wind. For relative humidity over the CONUS there are small differences
between the two NGM forecasts

Over Alaska (Fig. 11) the difference or spread between the operational and parallel NGM
vector wind RMS errors is similar to that observed over the CONUS in Fig. 9. This indicates that
the absence of an analysis update on the coarse resolution A-grid does not disproportionally
worsen forecast skill in regions close to the A-grid/B-grid interface.

b. Fits to Rawinsondes - Warm Season

The vertical distribution of RMS error versus rawinsondes over the CONUS for the 12-h,
24-h, and 48-h operational and parallel NGM forecasts from 2 July 1999 - 2 August 1999 are
shown in Fig. 12 (height), Fig. 13 (temperature), Fig. 14 (vector wind) and Fig. 15 (relative
humidity. For heights, RMS errors for the parallel NGM are higher at 12-h and 24-h, similar to the
cool season results, but are lower than the operational NGM by 48-h. The warm-season RMS
temperature errors are uniformly lower in the parallel NGM than in the operational NGM through
most of the middle and upper troposphere. Vector wind errors are slightly lower in the parallel run
at most pressure levels, while the relative humidity errors are generally lower in the first 24-h of
the parallel NGM forecast. This could reflect the improvement in the initial moisture in the parallel
NGM through the use of GOES and SSM/I precipitable water data in the Eta analysis.

c. Fits to surface temperature data

For NWS field forecasters and private users one of the most important products from the
NGM is the MOS guidance, which includes predicted temperatures every 3-h at station locations
over North America. TDL is producing the full MOS package from the parallel NGM run as part
of the overall evaluation. Although TDL's results are not available as this bulletin is being written,
a possible hint to NGM MOS temperature performance can be inferred from the cumulative fit of
the operational and parallel NGM surface temperature forecasts to surface observations over the
CONUS, shown in the tables below:

Overall, the differences in the surface temperature errors are small, with the mean bias difference
no greater that 0.36oC. The performance of the parallel NGM tends to be slightly worse in the
warm season, which may reflect a warm bias in the Eta model (which would transmitted to the
parallel NGM through 3-h Eta model forecasts during the Eta Data Assimilation System (EDAS)).

d. 24-h Accumulated Precipitation - Bias and Equitable Threat Scores

The bias score and equitable threat score for operational and parallel NGM forecasts of 24-h accumulated precipitation are presented in Fig. 16 and Fig. 17, respectively, for the period 12Z
12/16/1999 - 00Z 2/14/2000. Over the CONUS, there is a 5-10% decrease in both skill scores for
the cool season test. In an attempt to gain more insight to the degradation in forecast precipitation
skill, equitable threat scores were computed for the two regions (western and eastern U.S.) shown
in Fig. 18. From these regional scores (Figs 19 and 20) it is clear that almost all the degradation is
occurring in the western U.S. Three factors may be contributing this degradation: possible
problems with the Eta 3DVAR analysis is the eastern Pacific, analysis uncertainty in the eastern
Pacific due to the earlier data cutoff time, and a less accurate forecast on the A-grid in the parallel
NGM, since no analysis update is done. Any error in the A-grid forecast would be propagated to
the B-grid via the two-way interactive boundary condition interaction between the A-grid and he
B-grid.

An example which illustrates the above problem is seen in the operational (Fig. 21) and
parallel (Fig. 22) NGM forecasts of 24-h accumulated precipitation valid at 12Z 2/4/2000. This
case (Cairns, personal communication), was notable in that the Eta model (not shown) failed to
predict snowfall over the crest of the Sierra Nevada mountains in California, while the operational
NGM did predict precipitation in this region. As seen in Figs. 21 and 22, the operational NGM
predicted a much stronger offshore precipitation band, while the parallel NGM did not predict
precipitation over the Sierra Nevada region at all, similar to the Eta forecast (not shown).
Examination of the Eta moisture analysis (not shown) revealed that at 12Z 2/3/2000 the Eta
3DVAR analysis decreased the deep layer moisture (when compared to the EDAS first guess) by
5-10% along the southern part of the offshore frontal boundary. At 500 mb (not shown) the
offshore low/trough in the 00, 12, and 24-h forecasts was ~60 m deeper in the operational NGM.
Since the parallel NGM precipitation forecast closely resembles the Eta forecast, it is clear that Eta
analysis deficiencies over the Pacific is the probable reason for the differences between the NGM
precipitation forecasts. It is hoped that the direct assimilation of satellite radiance data in the Eta
3DVAR analysis (planned to be operational by fall 2000) will help alleviate the persistent problems
seen in the eastern Pacific during the cool season, with positive benefits for the NGM forecasts
initialized from this analysis.

The precipitation scores for the warm season parallel (Figs 23 and 24) show smaller
differences between the operational and parallel NGM forecasts then was seen in the cool season
scores described above. The eastern and western U.S scores for the warm season test (not shown)
at http://sgi62.wwb.noaa.gov:8080/NGMSTATS/ reveal that although the eastern U.S. scores are
better than in the west, there is much less degradation in the western U.S. then was seen in the
cool season scores. The greater degradation seen in the western U.S. during the cool season
reinforces the impression that the EDAS/Eta has problems in the eastern Pacific, especially with
major precipitation producing systems. Although the other changes in the parallel NGM (smaller
B-grid, A-grid initialized from a 6-h forecast) may also contribute to this problem, they are
probably not a major factor given the lack of severe degradation seen in Alaska as described above.

V. Forecast example : the East Coast Storm of 24-25 January 2000

The storm of 24-25 January 2000 produced severe winter weather in all major metropolitan
areas of the eastern United States, extending from North Carolina into New England. Snowfall
totals in excess of 12 inches were reported at many locations throughout the mid-Atlantic states.
Although the NCEP short-range models did predict off-shore cyclogenesis, model forecasts
initialized 12-h to 24-h prior to the event failed to predict the location and intensity of the
precipitation over the heavily populated areas of the northeastern U.S.

Fig. 25 shows the 24-h forecast precipitation valid at 12Z 1/25/2000 from the operational
and parallel runs of the NGM. Although neither forecast predicted precipitation in the Baltimore /
Washington area (which ultimately received 6-19 inches of snow), the parallel NGM forecast did
predict heavier amounts over the Delmarva peninsula and extreme southeastern Virginia. The
parallel NGM predicted the surface cyclone to be much closer to the coastline than the operational
NGM (Fig. 26). Big differences are seen at 500 mb (Fig. 27), with a stronger vorticity maximum
associated with the oceanic cyclone and with the upstream short-wave trough over the Great
Lakes. In general, the parallel NGM resembled the forecast from the operational Eta (not shown).
A detailed analysis of NCEP model performance for this storm can be found at
http://www.emc.ncep.noaa.gov/mmb/research/blizz2000 .

VI. Conclusion

The performance of the IBM-SP version of the RAFS, in which the NGM is initialized
from the operational Eta analysis, has been described in this bulletin. During the warm season test,
there were small differences between the operational and parallel NGM forecasts when verified
against rawinsonde data, 24-h observed precipitation data, and surface temperature observations.
During the cool season, the results vary by pressure level and by region. In the lower and middle
troposphere over the CONUS and Alaska, there were small differences in the error statistics versus
rawinsondes between the two NGM forecasts. Over the CONUS there was little difference in the
fit of the parallel NGM forecasts to the surface temperature observations. In the eastern U.S. there
were small differences between the operational and parallel NGM forecast precipitation skill
scores. In the western U.S., the parallel NGM did show less skill in forecasting precipitation, which
is probably due (in order of importance) to: 1) deficiencies in the Eta analysis over the eastern
Pacific during the winter months, 2) fewer oceanic observations ingested in the Eta analysis due to
the earlier data cutoff time, and 3) no analysis update on the 160 km NGM A-grid. One of EMC's
primary goals in the next 6-12 month period is to address the problems in the EDAS by testing
(and hopefully introduce into the EDAS) direct assimilation of satellite radiances and assimilation
of observed cloud and precipitation.

The date of the operational implementation of this version of the RAFS is still pending,
awaiting results from TDL's evaluation and final approval from the NWS Committee on Analysis
and Forecast Techniques Implementation (CAFTI). If this approval is given implementation on the
IBM-SP will occur sometime in March-April 2000.